Semi-supervised Constrained Hidden Markov Model Using Multiple Sensors for Remaining Useful Life Prediction and Optimal Predictive Maintenance for Remaining Useful Life Prediction and Optimal Predictive Maintenance

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Published Sep 22, 2019
Xinyu Zhao Yunyi Kang Hao Yan https://orcid.org/0000-0002-4322-7323 Feng Ju

Abstract

Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we develop a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is effective in estimating the RUL, while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov Decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method.

How to Cite

Zhao, X., Kang, Y., Yan, H., & Ju, F. (2019). Semi-supervised Constrained Hidden Markov Model Using Multiple Sensors for Remaining Useful Life Prediction and Optimal Predictive Maintenance: for Remaining Useful Life Prediction and Optimal Predictive Maintenance. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.851
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Keywords

Hidden Markov Model, Partially Observed Markov Decision Process, Remaining Useful Life, Predictive Maintenance

Section
Technical Papers